The following chart caught my eye when it appeared in the Wall Street Journal this month:
This is a laborious design; much sweat has been poured into it. It's a chart that requires the reader to spend time learning to read.
A major difficulty for any visualization of this dataset is keeping track of the two time scales. One scale, depicted horizontally, traces the dates of Fed meetings. These meetings seem to occur four times a year except in 2012. The other time scale is encoded in the colors, explained above the chart. This is the outlook by each Fed committee member of when he/she expects a rate hike to occur.
I find it challenging to understand the time scale in discrete colors. Given that time has an order, my expectation is that the colors should be ordered. Adding to this mess is the correlation between the two time scales. As time treads on, certain predictions become infeasible.
Part of the problem is the unexplained vertical scale. Eventually, I realize each cell is a committee member, and there are 19 members, although two or three routinely fail to submit their outlook in any given meeting.
Contrary to expectation, I don't think one can read across a row to see how a particular member changes his/her view over time. This is because the patches of color would be less together otherwise.
After this struggle, all I wanted is some learning from this dataset. Here is what I came up with:
There is actually little of interest in the data. The most salient point is that a shift in view occurred back in September 2012 when enough members pushed back the year of rate hike that the median view moved from 2014 to 2015. Thereafter, there is a decidedly muted climb in support for the 2015 view.
This is an example in which plotting elemental data backfires. Raw data is the sanctuary of the incurious.